This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0003
Accuracy: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3804	1.0	65	0.1751	0.9653
0.1041	2.0	130	0.0557	0.9805
0.071	3.0	195	0.0241	0.9924
0.0687	4.0	260	0.0198	0.9967
0.0851	5.0	325	0.0278	0.9892
0.0383	6.0	390	0.0075	0.9978
0.0485	7.0	455	0.0115	0.9967
0.0781	8.0	520	0.0157	0.9924
0.0374	9.0	585	0.0075	0.9967
0.0435	10.0	650	0.0052	0.9989
0.0309	11.0	715	0.0105	0.9957
0.0464	12.0	780	0.0085	0.9967
0.0189	13.0	845	0.0032	1.0
0.0156	14.0	910	0.0065	0.9967
0.0207	15.0	975	0.0018	1.0
0.0337	16.0	1040	0.0065	0.9967
0.0313	17.0	1105	0.0009	1.0
0.027	18.0	1170	0.0032	1.0
0.0142	19.0	1235	0.0017	1.0
0.0113	20.0	1300	0.0020	1.0
0.0275	21.0	1365	0.0030	1.0
0.0102	22.0	1430	0.0007	1.0
0.0191	23.0	1495	0.0102	0.9978
0.0092	24.0	1560	0.0037	0.9989
0.0157	25.0	1625	0.0012	1.0
0.0117	26.0	1690	0.0014	1.0
0.0114	27.0	1755	0.0003	1.0
0.0152	28.0	1820	0.0009	1.0
0.0124	29.0	1885	0.0015	0.9989
0.0093	30.0	1950	0.0033	0.9989
0.0094	31.0	2015	0.0027	0.9989
0.0242	32.0	2080	0.0026	0.9989
0.0021	33.0	2145	0.0012	0.9989
0.0074	34.0	2210	0.0020	0.9989
0.0065	35.0	2275	0.0019	0.9989
0.0067	36.0	2340	0.0046	0.9989
0.0084	37.0	2405	0.0016	0.9989
0.0125	38.0	2470	0.0011	1.0
0.0093	39.0	2535	0.0021	0.9989
0.0037	40.0	2600	0.0004	1.0
0.0082	41.0	2665	0.0011	0.9989
0.0071	42.0	2730	0.0010	1.0
0.0183	43.0	2795	0.0037	0.9989
0.0071	44.0	2860	0.0004	1.0
0.0032	45.0	2925	0.0002	1.0
0.0052	46.0	2990	0.0001	1.0
0.007	47.0	3055	0.0002	1.0
0.0026	48.0	3120	0.0002	1.0
0.0089	49.0	3185	0.0003	1.0
0.0031	50.0	3250	0.0003	1.0

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.1

shubham91/beit-base-patch16-224-pt22k-finetuned-eurosat

beit-base-patch16-224-pt22k-finetuned-eurosat

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

shubham91/
beit-base-patch16-224-pt22k-finetuned-eurosat